chore: import upstream snapshot with attribution

python/dgl/data/wikics.py

@@ -0,0 +1,173 @@
+"""Wiki-CS Dataset"""
+import itertools
+import json
+import os
+
+import numpy as np
+
+from .. import backend as F
+from ..convert import graph
+from ..transforms import reorder_graph, to_bidirected
+from .dgl_dataset import DGLBuiltinDataset
+from .utils import _get_dgl_url, generate_mask_tensor, load_graphs, save_graphs
+
+
+class WikiCSDataset(DGLBuiltinDataset):
+    r"""Wiki-CS is a Wikipedia-based dataset for node classification from `Wiki-CS: A Wikipedia-Based
+    Benchmark for Graph Neural Networks <https://arxiv.org/abs/2007.02901v2>`_
+
+    The dataset consists of nodes corresponding to Computer Science articles, with edges based on
+    hyperlinks and 10 classes representing different branches of the field.
+
+    WikiCS dataset statistics:
+
+    - Nodes: 11,701
+    - Edges: 431,726 (note that the original dataset has 216,123 edges but DGL adds
+      the reverse edges and removes the duplicate edges, hence with a different number)
+    - Number of classes: 10
+    - Node feature size: 300
+    - Number of different train, validation, stopping splits: 20
+    - Number of test split: 1
+
+    Parameters
+    ----------
+    raw_dir : str
+        Raw file directory to download/contains the input data directory.
+        Default: ~/.dgl/
+    force_reload : bool
+        Whether to reload the dataset.
+        Default: False
+    verbose : bool
+        Whether to print out progress information.
+        Default: False
+    transform : callable, optional
+        A transform that takes in a :class:`~dgl.DGLGraph` object and returns
+        a transformed version. The :class:`~dgl.DGLGraph` object will be
+        transformed before every access.
+
+    Attributes
+    ----------
+    num_classes : int
+        Number of node classes
+
+    Examples
+    --------
+    >>> from dgl.data import WikiCSDataset
+    >>> dataset = WikiCSDataset()
+    >>> dataset.num_classes
+    10
+    >>> g = dataset[0]
+    >>> # get node feature
+    >>> feat = g.ndata['feat']
+    >>> # get node labels
+    >>> labels = g.ndata['label']
+    >>> # get data split
+    >>> train_mask = g.ndata['train_mask']
+    >>> val_mask = g.ndata['val_mask']
+    >>> stopping_mask = g.ndata['stopping_mask']
+    >>> test_mask = g.ndata['test_mask']
+    >>> # The shape of train, val and stopping masks are (num_nodes, num_splits).
+    >>> # The num_splits is the number of different train, validation, stopping splits.
+    >>> # Due to the number of test spilt is 1, the shape of test mask is (num_nodes,).
+    >>> print(train_mask.shape, val_mask.shape, stopping_mask.shape)
+    (11701, 20) (11701, 20) (11701, 20)
+    >>> print(test_mask.shape)
+    (11701,)
+    """
+
+    def __init__(
+        self, raw_dir=None, force_reload=False, verbose=False, transform=None
+    ):
+        _url = _get_dgl_url("dataset/wiki_cs.zip")
+        super(WikiCSDataset, self).__init__(
+            name="wiki_cs",
+            raw_dir=raw_dir,
+            url=_url,
+            force_reload=force_reload,
+            verbose=verbose,
+            transform=transform,
+        )
+
+    def process(self):
+        """process raw data to graph, labels and masks"""
+        with open(os.path.join(self.raw_path, "data.json")) as f:
+            data = json.load(f)
+        features = F.tensor(np.array(data["features"]), dtype=F.float32)
+        labels = F.tensor(np.array(data["labels"]), dtype=F.int64)
+
+        train_masks = np.array(data["train_masks"], dtype=bool).T
+        val_masks = np.array(data["val_masks"], dtype=bool).T
+        stopping_masks = np.array(data["stopping_masks"], dtype=bool).T
+        test_mask = np.array(data["test_mask"], dtype=bool)
+
+        edges = [[(i, j) for j in js] for i, js in enumerate(data["links"])]
+        edges = np.array(list(itertools.chain(*edges)))
+        src, dst = edges[:, 0], edges[:, 1]
+
+        g = graph((src, dst))
+        g = to_bidirected(g)
+
+        g.ndata["feat"] = features
+        g.ndata["label"] = labels
+        g.ndata["train_mask"] = generate_mask_tensor(train_masks)
+        g.ndata["val_mask"] = generate_mask_tensor(val_masks)
+        g.ndata["stopping_mask"] = generate_mask_tensor(stopping_masks)
+        g.ndata["test_mask"] = generate_mask_tensor(test_mask)
+
+        g = reorder_graph(
+            g,
+            node_permute_algo="rcmk",
+            edge_permute_algo="dst",
+            store_ids=False,
+        )
+
+        self._graph = g
+
+    def has_cache(self):
+        graph_path = os.path.join(self.save_path, "dgl_graph.bin")
+        return os.path.exists(graph_path)
+
+    def save(self):
+        graph_path = os.path.join(self.save_path, "dgl_graph.bin")
+        save_graphs(graph_path, self._graph)
+
+    def load(self):
+        graph_path = os.path.join(self.save_path, "dgl_graph.bin")
+        g, _ = load_graphs(graph_path)
+        self._graph = g[0]
+
+    @property
+    def num_classes(self):
+        return 10
+
+    def __len__(self):
+        r"""The number of graphs in the dataset."""
+        return 1
+
+    def __getitem__(self, idx):
+        r"""Get graph object
+
+        Parameters
+        ----------
+        idx : int
+            Item index, WikiCSDataset has only one graph object
+
+        Returns
+        -------
+        :class:`dgl.DGLGraph`
+
+            The graph contains:
+
+            - ``ndata['feat']``: node features
+            - ``ndata['label']``: node labels
+            - ``ndata['train_mask']``: train mask is for retrieving the nodes for training.
+            - ``ndata['val_mask']``: val mask is for retrieving the nodes for hyperparameter tuning.
+            - ``ndata['stopping_mask']``: stopping mask is for retrieving the nodes for early stopping criterion.
+            - ``ndata['test_mask']``: test mask is for retrieving the nodes for testing.
+
+        """
+        assert idx == 0, "This dataset has only one graph"
+        if self._transform is None:
+            return self._graph
+        else:
+            return self._transform(self._graph)